Traffic Flow Management Research Articles

A Large Neighborhood Search Algorithm with Simulated Annealing and Time Decomposition Strategy for the Aircraft Runway Scheduling Problem

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) is of great significance to improve the utilization of runways and reduce aircraft delays. This paper proposes a large neighborhood search algorithm combined with simulated annealing and the receding horizon control strategy (RHC-SALNS) which is used to solve the ARSP. In the framework of simulated annealing, the large neighborhood search process is embedded, including the breaking, reorganization and local search processes. The large neighborhood search process could expand the range of the neighborhood building in the solution space. A receding horizon control strategy is used to divide the original problem into several subproblems to further improve the solving efficiency. The proposed RHC-SALNS algorithm solves the ARSP instances taken from the actual operation data of Wuhan Tianhe Airport. The key parameters of the algorithm were determined by parametric sensitivity analysis. Moreover, the proposed RHC-SALNS is compared with existing algorithms with excellent performance in solving large-scale ARSP, showing that the proposed model and algorithm are correct and efficient. The algorithm achieves better optimization results in solving large-scale problems.

Retracted: Intelligent Management of Air Traffic Flow Based on Intelligent Motion Coordinate Data Model

Retracted: Intelligent Management of Air Traffic Flow Based on Intelligent Motion Coordinate Data Model

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.

Інтелектуалізація управління дорожнім рухом як засіб підвищення ефективності транспортної мережі міста в неординарних ситуаціях

An assessment of modern trends in the development of intelligent traffic management systems and their role in ensuring the efficiency of the functioning of transport networks was carried out. The processes of introducing technologies for expanding the flow of processed data into existing intelligent transport systems (ITS) that ensure an increase in the speed of information transmission in them have been determined. The classification of information sources that become available when the ITS transitions to the 5G standard and provide a basis for the implementation of technologies for avoiding extraordinary situations in transport networks is given.Existing methods of improving the efficiency of the city's transport network are mainly aimed at ensuring the ability of ITS to predict traffic flows. These include statistical and nonlinear methods, simulation-based methods, artificial intelligence methods, and combined methods. The implementation of these methods is achieved by increasing the information flow coming from the system. A comparison of these methods revealed that they can generally make predictions with high accuracy, however, regardless of the chosen standard, some of them are already at the peak of their potential in terms of application in ITS, and the rest still have room for development.The suitability of the forecasting method for working in real-time conditions is a significant advantage in ensuring effective management of traffic flows, allows to increase the stability of the transport network and the efficiency of the ITS, and has a positive effect on the level of traffic jams, road safety and ecological impact on the environment. The most promising in terms of a quick and flexible solution to an extraordinary situation are models with the use of artificial intelligence or a combination thereof, based on deep learning algorithms, which have proven their importance in predicting the results, making decisions regarding traffic flow forecasts and ensuring the elimination and avoidance of traffic jams based on the passage of vehicles through the intersection depending on the length and duration of the traffic light signals.

Transport Stream Optimization Based on Neural Network Learning Algorithms

The article discusses a description of the practical application of traffic flow management automation technologies and the use of artificial intelligence to solve traffic optimization problems, traffic monitoring, describes its goals, tasks, indicates the necessary functionality of the traffic monitoring automation system. The defining performance indicators of the use of automated traffic control systems are described. Various methods of calculations used in the control, regulation, analysis and management of traffic flows are demonstrated. The gradations of service levels and levels of convenience in estimating throughput are considered. Analyzed various criteria for the regulation and control of road and transport situations in general.

ПРОГРАММНЫЙ ПОДХОД К КОМПЛЕКСНОМУ СБОРУ И ПОДГОТОВКИ ДАННЫХ ОБ ИНТЕНСИВНОСТИ ДВИЖЕНИЯ ТРАНСПОРТНЫХ СРЕДСТВ, ПОГОДНЫХ УСЛОВИЙ И ЕСТЕСТВЕННОЙ ОСВЕЩЕННОСТИ В ЧАСОВЫХ ИНТЕРВАЛАХ

The steady growth in the level of motorization inevitably leads to congestion of the elements of the road network in the cities of our country. The intensity movements of road transport is an important indicator of accidents, as it determines the modes and conditions for the occurrence of accident-hazardous areas. At the same time, the state of controlled intersections is the most relevant. The consequence of an accident is a large number of factors of the Driver-Car-Road-Environment system. To predict this negative phenomenon, which is considered a failure in a harmoniously functioning transport infrastructure, it is necessary to study the accompanying conditions and their influence on each other in detail. Thus, in order to carry out the necessary reconstruction measures at emergency hazardous areas, including at controlled intersections, it is necessary to carry out high-quality calculations aimed at evaluating the effectiveness of the proposed measures, as well as designing control modes at intersections with the optimal configuration of elements. This article discusses a software approach to the integrated collection and preparation of data, the use of which makes it possible, on the basis of the obtained patterns, to solve the problems of harmonious, without significant failures, the functioning of any considered transport hub on the road network. These studies are a new round in the improvement of the road safety system, striving for zero deaths on roads. The complex solution, in the version proposed by the authors of the article, has a direct impact on the management of traffic flows in an accident-free mode, which has not been previously developed and applied, this is the scientific novelty of the work.

Actualization of technological interaction managing methods in portside transport systems

Methodological approaches to the assessment of the portside transport and technological system development prospects in terms of interaction management in the “railway station - port” system are being studied. The method of port wagon flow distribution on the basis of combined application of analytical and simulation modeling, probabilistic-statistical and censorological approach is presented. An algorithm for the formation and assessment of the transport process axiom in the “railway station - port” system is formed. The approximations of power functions in the technocenosis of port station parameters are determined. Scientific methods, based on linear and dynamic programming, mass service theory, reliability theory, graph theory, probability theory, simulation modeling, etc. are widely used in the management of traffic flows. To describe the regularities of transport processes, the notions: individual, species, population, etc. are used in CA of the system “railway station - port”. The structural elements of the portside transport system, evaluated by means of CA, are presented in аrticle.

Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing machine learning techniques in the management of traffic flow. Using the Web of Science (WOS) platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using machine learning techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that machine learning can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.

Road-Type Classification with Deep AutoEncoder.

Machine learning algorithms are among the driving forces towards the success of intelligent road network systems design. Such algorithms allow for the design of systems that provide safe road usage, efficient infrastructure, and traffic flow management. One such application of machine learning in intelligent road networks is classifying different road network types that provide useful traffic information to road users. We propose a deep autoencoder model for representation learning to classify road network types. Each road segment node is represented as a feature vector. Unlike existing graph embedding methods that perform road segment embedding using the neighbouring road segments, the proposed method performs embedding directly on the road segment vectors. The proposed method performs embedding directly on the road segment vectors. Comparison with state-of-the-art graph embedding methods show that the proposed method outperforms graph convolution networks, GraphSAGE-MEAN, graph attention networks, and graph isomorphism network methods, and it achieves similar performance to GraphSAGE-MAXPOOL.

Pedestrian Flows Analysis to Create a Sustainable Development Concept for a Metropolitan Area

This article presents the results of structuring the territory adjacent to Pryanishnikov Street in the Timiryazevsky District of the Northern Administrative District of Moscow. The key objects of the street road network that form the pedestrian flows are highlighted. The rules of interaction between pedestrian flows and the influence of external factors have been established. Based on this we constructed undirected graphs reflecting the connectivity of pedestrian routes, the influence of infrastructure objects on them, and the density of flows depending on the time of day and days of the week. Such results are the basis for simulation modeling, allowing to obtain an adequate model to assess the impact of decisions on the area development, and the operational management of traffic and pedestrian flows of the street road network.

Improving the Traffic Management System Based on Remote Sensing Data Imagery

Traffic management aims to increase or maintain the standard of transport services. The use of intelligent transport systems, such as traffic signals and variable message signs, can aid in the efficient management of traffic flow. Traffic management is a relatively inexpensive form of road management, but its impact is generally less studied. This study evaluated the level of road services (LOS) provided by Prof. Dr. Ir. Herman Yohanes Road based on the fusion of remote sensing data imagery and field surveys. The study found the LOS before the traffic management to be category E LOS, but it increased into category C after the traffic management. The increase heavily implied by the road capacity which shown the decrease of volume of vehicles from 3.030 to 993 and an increase of vehicle’s speed from 25 km/h to 40–60 km/h. Questionnaires were used to assess the effects of traffic management on road satisfaction and revenue changes among selected business along Prof. Dr. Ir. Herman Yohanes Road. The increase in LOS seems to also increase the road satisfaction due to less volume of vehicles and better travel speed. Despite the increase on both LOS and road satisfaction, annual revenue seemed to decrease for 20% on micro business, 50% on small business, 19,4% on medium business, and 40% on big business. The findings also include a detailed level of service road map indicating the differences of before and after the implementation of traffic management measures

Mathematical methods and models of traffic flow management

An overview of existing mathematical models of traffic flows is presented. Various macroscopic and microscopic mathematical models of traffic flows are considered, which make it possible to calculate the congestion of road transport networks. The basic principles of their application and significant differences between them are analyzed. Based on the considered mathematical models, a model of rebuilding vehicles between traffic lanes is proposed.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.

Research on Air Traffic Flow Management Delay Distribution Prediction Based on IV Value and PSO-SVM

Research on Air Traffic Flow Management Delay Distribution Prediction Based on IV Value and PSO-SVM

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with ATM

Impact on Platooning of Autonomous Vehicles using Macro to Micro simulations

Social costs increase as traffic problems continue. Driverless vehicles are attracting attention as a way to overcome this issue. Many said that fully autonomous driving is not a distant future. As one of the autonomous vehicle operations, vehicle platooning has been paid attention. Therefore, this study analyzes the effects of vehicle platooning on the road network. As a large-scale network, Jeju island, one of the cities in Korea was selected to test the platooning arrangement. A macro simulation was developed and calibrated based on the collected traffic counts, and then the network and assigned paths were imported to the micro simulation, which is capable of forming the platoon. The analysis was conducted by changing various parameters of the platooning vehicle. According to the investigation, it can be seen that as the market share of autonomous vehicles increases, delays decrease, and speed increases. Traffic efficiency was improved through platooning in general. It is expected that the results of this study can be used as primary data to develop an optimal traffic flow management strategy for cluster operation.

РАЗВИТИЕ МЕТОДОВ УПРАВЛЕНИЯ ТРАНСПОРТНЫМИ ПОТОКАМИ В МАЛЫХ И СРЕДНИХ ГОРОДАХ

The article is devoted to the development of traffic flow management methods in urban transport systems of small and medium-sized cities. Certain conditions for managing traffic flows, typical for small and medium-sized cities, require the development of methods used today. In order to improve management efficiency, in particular, the use of coordinated management of main streets, within the framework of this study, the movement process was modeled and the delay time was estimated when the intensity changed at the object of study. The analysis of the obtained results made it possible to establish certain dependencies and identify specialized areas in which it is necessary to change the type of management used.

Method of Vertiport Capacity Assessment Based on Queuing Theory of Unmanned Aerial Vehicles

Urban air traffic has gradually attracted attention in recent years, which will bring endless vitality to future urban development. An objective and accurate method of assessing vertiport capacity is the basis for the air traffic flow management of UAVs, which plays an important role in improving the efficiency of urban airspace resources. First of all, this paper establishes a theoretical capacity calculation model for ground facilities such as takeoff and landing platforms, taxiways and aprons, respectively. Next, this paper analyzes the service characteristics of each ground facility and establishes different types of UAV queuing systems to obtain UAV delay curves based on a UAV Poisson flow arrival model. Subsequently, a suitable acceptable delay level is selected to obtain the corresponding UAV flow, which means the actual capacity of UAV operations. Eventually, the validity of the model is verified through actual drone data arithmetic examples. The calculation results show that the combination of “1 landing + 2 takeoffs” can achieve better capacity results and that the landing platform is more prone to congestion than the takeoff platform. Change in average service time has the greatest impact on the apron capacity, and the takeoff platform is the most sensitive to changes in the acceptable delay level.

A multi-view attention-based spatial–temporal network for airport arrival flow prediction

Accurate airport arrival flow prediction is a precondition for intelligent air traffic flow management. However, most existing studies focus on the dynamic traffic flow in a single-airport scenario, which usually ignores the spatial interactions among airports. Modelling network-wide spatial dependencies among airports is difficult because it requires models to consider multiple underlying factors jointly. We propose a multi-view fusion approach to automatically learn an adjacency matrix from flight duration and flight schedule factors. The learned adjacency matrix is then fed into a specially designed graph convolutional block, which governs the message passing process among airports. Finally, the graph convolutional block with the learned adjacency matrix is embedded into the gated recurrent units to capture temporal dependencies. Experimental results on a real-world dataset for the multistep prediction task show the effectiveness and efficacy of the proposed model. In addition, visualisation and analysis of the learned adjacency matrix verify that the proposed multi-view fusion approach is capable of learning informative spatial interaction patterns.

ОБЗОР МЕТОДОВ СБОРА ДАННЫХ ДЛЯ ОЦЕНКИ МАТРИЦЫ ПОЕЗДОК

Traffic flow management on the road network is one of the key areas of transport and logistics activities, which involves various enterprises, third-party companies and regulatory authorities. Traffic safety and regularity of trips directly depend on the quality and clarity of the organization of the transport and logistics process. In a competitive environment, assessing the strengths and weaknesses of existing data collection methods becomes a significant advantage, allowing us to apply those methods that allow us to achieve our goals. To solve this problem, it is proposed to jointly and consistently use methods for restoring the correspondence matrix within the framework of the technological process of transport and logistics hubs. Based on the results of the analysis, specific proposals have been developed, the implementation of which will improve the efficiency of the transportation process based on the technically competent development and implementation of a methodology for assessing transport demand